Collaborative Research: MLWiNS: Dino-RL: A Domain Knowledge Enriched Reinforcement Learning Framework for Wireless Network Optimization

合作研究:MLWiNS:Dino-RL:用于无线网络优化的领域知识丰富的强化学习框架

基本信息

  • 批准号:
    2002902
  • 负责人:
  • 金额:
    $ 18.51万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-06-01 至 2025-05-31
  • 项目状态:
    未结题

项目摘要

Reinforcement learning (RL) methods have met with renewed interest in recent years for adaptively configuring wireless networks. Despite the promising early results and the conceptual match, many existing approaches do not develop and tailor the RL methods to fit the unique characteristics of wireless networking. The goal of this project is to develop a novel domain knowledge enriched RL framework, or Dino-RL, to address this problem. The Dino-RL framework aims to seamlessly integrate the physical-law based modeling and an abstract episodic memory into the RL process, and has the potential to revamp the operation and management of future wireless networks. Developing this novel technology would also help maintain the nation's continued leadership in wireless technologies and its pipeline of highly qualified engineers. The project pursues synergistic activities for the successful design and implementation of Dino-RL, followed by a comprehensive, real-world data driven evaluation. Episodic RL is first studied with the objective to incorporate domain knowledge into building an efficient episodic memory. In addition, a hierarchical hidden variable model is built to enable meta-reinforcement learning for knowledge transfer and efficient exploration. Lastly, the conflict between enhancing the physical-law based modeling and reinforcement learning is balanced via novel sample-efficient model selection algorithms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
近年来,强化学习(RL)方法在自适应配置无线网络方面重新受到关注。尽管有很好的早期结果和概念匹配,许多现有的方法不开发和定制RL方法,以适应无线网络的独特特性。这个项目的目标是开发一个新的领域知识丰富的RL框架,或Dino-RL,以解决这个问题。Dino-RL框架旨在将基于物理定律的建模和抽象情景记忆无缝集成到RL过程中,并有可能改进未来无线网络的运营和管理。开发这项新技术还将有助于保持美国在无线技术和高素质工程师队伍中的持续领先地位。该项目追求协同活动,以成功设计和实施Dino-RL,然后进行全面的,真实世界的数据驱动评估。情景强化学习首先研究的目标是将领域知识纳入建立一个有效的情景记忆。此外,层次隐变量模型的建立,使元强化学习的知识转移和有效的探索。最后,通过新颖的样本效率模型选择算法,平衡了增强基于物理定律的建模和强化学习之间的冲突。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(23)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
On High-dimensional and Low-rank Tensor Bandits
关于高维低阶张量老虎机
Reward Teaching for Federated Multiarmed Bandits
  • DOI:
    10.1109/tsp.2023.3333658
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    5.4
  • 作者:
    Chengshuai Shi;Wei Xiong;Cong Shen;Jing Yang
  • 通讯作者:
    Chengshuai Shi;Wei Xiong;Cong Shen;Jing Yang
Cascading Bandits with Two-Level Feedback
具有两级反馈的级联 Bandits
Teaching Reinforcement Learning Agents via Reinforcement Learning
通过强化学习教授强化学习代理
Multi-Agent Reinforcement Learning for Wireless User Scheduling: Performance, Scalablility, and Generalization
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Cong Shen其他文献

Output-feedback stabilization control of systems with random switchings and state jumps
具有随机切换和状态跳跃的系统的输出反馈稳定控制
Multi-relation graph embedding for predicting miRNA-target gene interactions by integrating gene sequence information
通过整合基因序列信息预测 miRNA-靶基因相互作用的多关系图嵌入
Stochastic Linear Contextual Bandits with Diverse Contexts
具有不同上下文的随机线性上下文强盗
Stability analysis for interval time-varying delay systems based on time-varying bound integral method
基于时变界限积分法的区间时变时滞系统稳定性分析
  • DOI:
    10.1016/j.jfranklin.2014.07.015
  • 发表时间:
    2014-10
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Qian Wei;Li Tao;Cong Shen;Fei Shumin
  • 通讯作者:
    Fei Shumin
On the Design of Modern Multilevel Coded Modulation for Unequal Error Protection
论现代多级编码调制的不等差错保护设计

Cong Shen的其他文献

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{{ truncateString('Cong Shen', 18)}}的其他基金

Collaborative Research: CPS Medium: Learning through the Air: Cross-Layer UAV Orchestration for Online Federated Optimization
合作研究:CPS 媒介:空中学习:用于在线联合优化的跨层无人机编排
  • 批准号:
    2313110
  • 财政年份:
    2023
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Standard Grant
CAREER: Towards a Communication Foundation for Distributed and Decentralized Machine Learning
职业:为分布式和去中心化机器学习建立通信基础
  • 批准号:
    2143559
  • 财政年份:
    2022
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Continuing Grant
CCSS: Collaborative Research: Towards a Resource Rationing Framework for Wireless Federated Learning
CCSS:协作研究:无线联邦学习的资源配给框架
  • 批准号:
    2033671
  • 财政年份:
    2020
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Standard Grant
Collaborative Research: SWIFT: SMALL: Learning-Efficient Spectrum Access for No-Sensing Devices in Shared Spectrum
合作研究:SWIFT:SMALL:共享频谱中无感知设备的学习高效频谱访问
  • 批准号:
    2029978
  • 财政年份:
    2020
  • 资助金额:
    $ 18.51万
  • 项目类别:
    Standard Grant

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相似海外基金

Collaborative Research: MLWiNS: Distributed Learning over Multi-Access Channels: From Bandlimited Coordinate Descent to Gradient Sketching
协作研究:MLWiNS:多访问通道上的分布式学习:从带限坐标下降到梯度草图
  • 批准号:
    2203412
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    2021
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协作研究:MLWiNS:一种以编码为中心的方法,通过无线实现稳健、安全和私密的分布式学习
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    2020
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Collaborative Research: MLWiNS: A Coding-Centric Approach to Robust, Secure, and Private Distributed Learning over Wireless
协作研究:MLWiNS:一种以编码为中心的方法,通过无线实现稳健、安全和私密的分布式学习
  • 批准号:
    2002874
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合作研究:MLWiNS:干扰有限无线网络的 ANN
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Collaborative Research: MLWiNS: Dino-RL: A Domain Knowledge Enriched Reinforcement Learning Framework for Wireless Network Optimization
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    Standard Grant
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